TRIZ theory is considered as a kind of innovative theory, which mainly functions in solving contradiction and aims to improve engineers creativity. It may reason on different support such as web and scientific documents. Most of the current knowledge, especially in industrial domains lies in patents. They are, nevertheless, an underused resource because of their complexity, their length and the need of domain knowledge to make use of patent. In this paper, we introduce an innovative application of deep learning to mine knowledge from patents. We show that our summarization based approach, called Sum-maTRIZ 1 , enables the extraction of these contradictions essential for TRIZ problem solving engine. A BERT based summarization approach is introduced to retain contradiction sentences. Our approach is experimentally evaluated on a real data set showing the performance of SummaTRIZ.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.